Deep Residual UNet.ipynb · xuzhiyang483/Deep-Residual-Unet

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Deep Residual UNet.ipynb 248.59 KB

{
 "cells": [
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "# Deep Residual U-Net (ResUNet)\n",
 "Paper: https://arxiv.org/pdf/1711.10684.pdf"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models."
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## 1.1 Advantages\n",
 "<ol>\n",
 " <li>The residual unit will ease the training of the network.</li>\n",
 " <li>The skip connections within a residual unit and between low levels and high levels of the network will facilitate information propagation without degradation,making it possible to design a neural network with much fewer\n",
 "parameters.</li>\n",
 "</ol>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## 1.2 Architecture"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "### 1.2.1 Basic Buildig Block\n",
 "The ResUNet uses Residual Units as basic building block instead of plain convolutional block.\n",
 " \n",
 "<ol>\n",
 " <li>Residual units consists of:</li>\n",
 "<ol>\n",
 " <li>Two 3x3 convolutional block.</li>\n",
 " <li>An identity mapping.</li> \n",
 "</ol>\n",
 "\n",
 "The Identity Mapping connects the input and output of the Residual unit.\n",
 " \n",
 "\n",
 "<li>Convolutional block consists of:</li>\n",
 "<ol>\n",
 " <li>One Batch Normalization layer.</li>\n",
 " <li>One ReLU Activation layer.</li>\n",
 " <li>One Convolutional layer.</li>\n",
 "</ol>\n",
 "\n",
 "</ol>\n",
 " \n",
 "<img src=\"images/basic block.png\">"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "### 1.2.3 Complete Architecture\n",
 "\n",
 "The ResUNet consists of three parts:\n",
 "<ol>\n",
 " <li>Encoding</li>\n",
 " <li>Bridge</li>\n",
 " <li>Decoding</li>\n",
 "</ol>\n",
 "\n",
 "<ul>\n",
 "<li>\n",
 "In encoding units, instead of using pooling operation to downsample feature map size, a stride of 2 is applied to the first convolution block to reduce the feature map by half.\n",
 "</li>\n",
 "\n",
 "<li>\n",
 "Before each decoding unit, there is an up-sampling of feature maps from lower level and a concatenation with the feature maps from the corresponding encoding path.\n",
 "</li>\n",
 "\n",
 "<li>\n",
 "At last a 1x1 Convolution is applied with Sigmoid activation to obtain a desired segmentation map.\n",
 "</li>\n",
 "</ul>\n",
 "\n",
 "<img src=\"images/arch.png\">"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## 1.3 Loss\n",
 "Mean Squared Error(MSE) is used as the loss function\n",
 "\n",
 "<img src=\"images/loss.png\">\n",
 "\n",
 "<ul>\n",
 "<li>N = Number of training samples</li>\n",
 "<li>Net(Iw;W) = Predicted Segmentation Map</li>\n",
 "<li>si = Ground Tructh Segmentaton Map</li>\n",
 "</ul>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## 1.4 Result"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<img src=\"images/result.png\">"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## 1.4 Dataset\n",
 "Kaggle TGS Salt Identification Challenge: https://www.kaggle.com/c/tgs-salt-identification-challenge"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 1,
 "metadata": {},
 "outputs": [],
 "source": [
 "## Imports\n",
 "import os\n",
 "import sys\n",
 "import random\n",
 "\n",
 "import numpy as np\n",
 "import pandas as pd\n",
 "import cv2\n",
 "import matplotlib.pyplot as plt\n",
 "\n",
 "import tensorflow as tf\n",
 "from tensorflow import keras\n",
 "\n",
 "## Seeding \n",
 "seed = 2019\n",
 "random.seed = seed\n",
 "np.random.seed = seed\n",
 "tf.seed = seed"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## Path"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 2,
 "metadata": {},
 "outputs": [],
 "source": [
 "dataset_path = \"dataset/\"\n",
 "train_path = os.path.join(dataset_path, \"train/\")"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## Data Generator"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 3,
 "metadata": {},
 "outputs": [],
 "source": [
 "class DataGen(keras.utils.Sequence):\n",
 " def __init__(self, ids, path, batch_size=8, image_size=128):\n",
 " self.ids = ids\n",
 " self.path = path\n",
 " self.batch_size = batch_size\n",
 " self.image_size = image_size\n",
 " self.on_epoch_end()\n",
 " \n",
 " def __load__(self, id_name):\n",
 " ## Path\n",
 " image_path = os.path.join(self.path, \"images\", id_name) + \".png\"\n",
 " mask_path = os.path.join(self.path, \"masks\", id_name) + \".png\"\n",
 " \n",
 " ## Reading Image\n",
 " image = cv2.imread(image_path)\n",
 " image = cv2.resize(image, (self.image_size, self.image_size))\n",
 " \n",
 " ##Reading Mask\n",
 " mask = cv2.imread(mask_path, 0)\n",
 " mask = cv2.resize(mask, (self.image_size, self.image_size))\n",
 " mask = np.expand_dims(mask, axis=-1)\n",
 " \n",
 " ## Normalizaing \n",
 " image = image/255.0\n",
 " mask = mask/255.0\n",
 " \n",
 " return image, mask\n",
 " \n",
 " def __getitem__(self, index):\n",
 " if(index+1)*self.batch_size > len(self.ids):\n",
 " self.batch_size = len(self.ids) - index*self.batch_size\n",
 " \n",
 " files_batch = self.ids[index*self.batch_size : (index+1)*self.batch_size]\n",
 " \n",
 " image = []\n",
 " mask = []\n",
 " \n",
 " for id_name in files_batch:\n",
 " _img, _mask = self.__load__(id_name)\n",
 " image.append(_img)\n",
 " mask.append(_mask)\n",
 " \n",
 " image = np.array(image)\n",
 " mask = np.array(mask)\n",
 " \n",
 " return image, mask\n",
 " \n",
 " def on_epoch_end(self):\n",
 " pass\n",
 " \n",
 " def __len__(self):\n",
 " return int(np.ceil(len(self.ids)/float(self.batch_size)))"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## Hyperparameters"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 4,
 "metadata": {},
 "outputs": [],
 "source": [
 "train_csv = pd.read_csv(dataset_path + \"train.csv\")\n",
 "train_ids = train_csv[\"id\"].values\n",
 "\n",
 "image_size = 128\n",
 "batch_size = 16\n",
 "\n",
 "val_data_size = 200\n",
 "\n",
 "valid_ids = train_ids[:val_data_size]\n",
 "train_ids = train_ids[val_data_size:]"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 5,
 "metadata": {},
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "(16, 128, 128, 3) (16, 128, 128, 1)\n"
 ]
 }
 ],
 "source": [
 "gen = DataGen(train_ids, train_path, batch_size=batch_size, image_size=image_size)\n",
 "x, y = gen.__getitem__(0)\n",
 "print(x.shape, y.shape)"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "metadata": {},
 "outputs": [
 {
 "data": {
 "text/plain": [
 "<matplotlib.image.AxesImage at 0x7f2a97a4d9b0>"
 ]
 },
 "execution_count": 8,
 "metadata": {},
 "output_type": "execute_result"
 },
 {
 "data": {
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ko9HI+He/S/OUFz0To9FId3d3ms/nKc+gOI6tV2E8HpsraK1W+9QzCREixOfHWQB9JpOxDBEw9sCNOgRTLlQl+NOTqUrH4p2nbgDxSqWSKjpCG0iyxqtSqZQyNPP0DlpuAJOFolgsGtAmSaLRaCTpwC+zUAF8fIZX7Egybn46nWq9XqvRaBhlNZ1OrSPXN3uhIPKfvVwuTcFD1izJQBe/Hqgn+HjfMEVhmsUEkK7X61Z4rVarqUWqWq1aHYDGJ1/8ZhckHf3u37x5ozdv3mgymdgCikslz3Oz2dhOotPppFxNQ4QI8bgI++AQIUKE+IrHWWT0ZItQMp7nnkwmmk6nms1mljV6K1syQVQzXrtOJu3tiL0WXTp6wKCcwWeHbtrTYSWVSsWKhlgreLUM3L10dJKkMOozYNwofQPUdrvVYrEwxYxvblqtVinFCdLNYrFo3jQUXz0Xz31E0YL3PrUIlD/cU+moo4dn55q9Lw7XxbF97wHvgWaDouL68e9/+/atbm5uFMexdQBDgXkdfbvd1vX1tS4uLj7VkRsiRIjPj7MBevhr6VCA9QVDeOp2u23vA4xQaUCF0GAERw+IA/Cez6d46KWaqG18d+hpERTA8/bHUCLeK8f701Nj8IZl3l4ZDp2iKaoalDZcpz8P/7MsbPV63XxofCMYMsbVamXcvC9KexsFahC+Ka1Wq9mCigTWN3XR64CR2Xg8NqoJMzfeP51OTVlDrYLnQxEcQL+8vNSzZ8/U6/VUr9dDZ2yIEF8gzgLoJaUKoc1mM6XJBpxms5kKhYLm87mm02lqOhIFRt9gJB2NtrxHjDfhouhHxgmAklH7ABABaYq27AJOf4bCqte+kz2TtZ9y9Y1GwwDd+7sgO/Uafe9CyTHg9r10k/f73QfWA/53n/2vVisNh0Nr7Lq4uNBisdB4PDZw5ty95l6STY5ifi2LBkA/n8+tnkAPxMXFhamOvL/Qixcv9PLlS9VqtU9dU4gQIR4XZwH0ZKs0Co3HY8vofDFvMpmo2WyaPJHMzzf7+G2/7/DkPT6T3e/3iuPYaA1vZYxcECCUjo1AUEb5fN68awB5376/3W4Vx7HJKL0ckmP5yVaoaGj24jOq1appznk/xWIybLpS8f4hy/e7ET91K5vNqtvtqlgsqlqtpugy6DAWVLphoaUmk0nKdgKZJKDNgs29PnXSpMu51+vZ/aXQSsGaLtqvfe1r6vV6tqidLr4hQoT4/DgboEfNkc1mTW4nyWxvsQFm/B3qElQ1gI53t/TmZ57GILPEXx7a5bTTk8zXu1f6cwLUADSoEygVmoKw+fVafq6VY9L5ywxbMnY09HDpfpKUp4jYyfC5ftA418quiKHdlUpFlUrFgB66DHqMnQe0Ep/jVT6cCx28frFgF1EsFlP2zplMRhcXF7q6ulKr1TJtPk1h/p5fX1+r0WjYri5EiBDvHl8Y6KMoeinp70m6kpRI+k6SJH8ziqL/XNJflHT38NZfeRil9gODzNu3vwNokqxhiuYkD35o1D01A0Bh1uWHiHspph9jhxzQu2iShUO9MKh6v9+rVCqp2+3q9evXBoa1Wk2NRsPOG97anwPHJNuG3oCTL5fLqaHm/udPs2KawqRjcxa7BKx9yejJxsfjsTWEVatVNZtNNRqNFNDT0ERRFTrI1wK87NRbJEAPseh5Cwv6Czabja6vr/XRRx+p1+uljM8oLnNdtVotRaWda2fsU34fQoR46nifjH4r6S8nSfI7URTVJf3zKIr+8cO//Y0kSf7aYw8EqPAF94U7Mtz9fq/Xr18b0Hp9t/dpZyIRmTY/D9fvLQq8iZn3bifbZifA65VKRZPJxAD5+vpa9/f39jmAFODNzoQOU79LgNfnvac7A/5tu93q4uLCrBFYABneAR/PhCvfWEX2Lcn4dTTs1WrV9On1ej1VFyFzZrGo1+u2Q2o0Ggb4BDUEaCbmB3S7XSuuovSRDgt7r9fTy5cv1ev1zMdfOlpB+D4KFm3mC5xpPNn3IUSIp44vDPRJkryR9Obhz9Moiv6lpBdf5FjIFeHS4YOl48Qo6ehF7t0aAUqAAYkhXD2gCpj4JikPKt7YDE4ecMNkDTMySdbJS9coPD8SQUm2sLBwwUdvNpuU2kRSyp8eB0jUMewGvMEaixmUR71eT02aYqIUUk8GjlPz8AVhSakmLqgapJEUZyuVitrtdmpwuKTUoBSO1Ww2dXFxYffS70a4x71ezxYOFFLQXryX3d1kMtFwOLRmtHOLp/w+hAjx1PEkHH0URd+Q9G9K+j8k/VFJvxRF0Z+R9M90yHKGP+zn2drDG/sCIlSE57f9n/2cUum4GACYeN9UKpUUJy7JMnboCHhp/gyd43l0wBfQA7jZKdBxKslAlzmu0CUURPl36di5yvXBg7OzAKBPJZHeGx8nTk8L4RlDJswxKOD64iz3kJoICx07rmKxqFarZfQXlhW9Xk/NZtMGu6B9Z2AM18p9hOah05njU6her9caj8eSZF3BSDbPFeh9vO/3IUSIp473BvooimqS/ntJ/3GSJJMoin5N0q/qwFP+qqS/LunPf8bPfVvSt6VDdhzHccokC7qE4iuvQX0Acvi5kJkCXFAdSPnK5XJKJSPJ6A6A3tsDeysFrwDis8iQPYWEzzw7ACZboZhpt9sqlUqpIipZsT/Gqf2DH+VHUPhktwI3juoGcOVcFotFyv8GOsTXJrgvvnkM/x3+TqH2FOjb7bbtKCqVihVZoaGoMXBd3hzO7y54bTg8YCGa++Vyee7UjaSn+T6ECPHU8V4WCFEU5XX4T/3fJknyP0hSkiQ3SZLskiTZS/pbkn7ms342SZLvJEnyrSRJvhW6HUN8FeKpvg9/eGcc4scl3kd1E0n6byT9yyRJ/iv3+rMHvlKS/qSkf/F5x4JP9pQM2SUZH3RBqVRKTZ0iY4TS8J7q3omSYiiZunTILGezmXK5nGWfFEA9d93tdiUd6A8sBJBO+qlT0Dyei/a0BXp67/fOz/piqx8byC7E8+a8HzmiN0bzVMlisTCqg2IzBmbw9OxS/Kxbuo7pI5COFBn3KkkSk0CyW2JnVKvVUqZnqHF4RqvVyqSyvguXjufZbGY7Ef8M/XSsc4un/D6ECPHU8T7UzR+V9Kcl/V9RFP2fD6/9iqRfjKLop3XYqn5X0l/6vAMBOoyrQyMvHbXu0qGAR/EO7tbL/aSjRp6i7H6/12w203A4tGN6Pf1isUi5Wvp5qqhImIxEUxHKG5QiLCLo0wHHXC5nAzo8UOFRv9/v1Ww27ZxoTvKDTLA09gsC94JFDrBm4WDBoT7BPeZ9fsA5FE4cx/b6bDYzWqnRaJis0XP13mKZSVvUGfDngaJicQG8J5OJqYOgY7zFM1QXz9EPVjnjhqkn+z6ECPHU8T6qm/9N0md9676QRpiMb7lcajKZWLZIQxR2Ad1u19QncRwbmJKln1rs4sNC562fGIW3jCTTacN9e+D0OvrowaKXrJMsm89arVY2SalUKqnT6aheryuOY1vQogcTN5qupEPmij3yeDxOFZNHo5Gdu5c1UrT1/jkALXp9BrhQT5COi6Dv0vVe9xR1N5uNZeYsKnyG995hAWg2m7bbgtunWLxcLo13Hw6HVhCezWZ2nGw2ax453HOkm75mco7x1N+HECGeMs7iW4Pqg2ImY/akowcMxcBGo2FZK4oSnB8lWeMVWShGWdAU3uteOjZj0fSEFYIvWFIA7Ha75rKJXh9aAVko4/MkWSs/AMjugs5PHCg5b5qLxuNxqjMWIKRxifejEqJYCwB7AzcauLAkQMLofem5N2TLm81G0+lU8/ncCri+aI2SiIx9Op3aIsaOA/UMcwS2261JPfHcn8/npuqhc9iPF+Q4LEieugoRIsTj4yyAngYlskSfffrB3PwdoGw2m8Y3Q++s12ubsORVM3D9aMk5FuPuyLTpNmXgB7I+6QD0NAsB6hwTgEX1Qvgxe9Pp1EDR2/lyD0qlkpml+Z0JnD8+MNLR9oB7Q9ZN1zBdxgA91gdk5Syq3hSN8+Y5eOvnKIpM+rrf721sIYFyh0WSSVar1coWPxYs6DgUUchD/bDy04lhvgs3RIgQ7xZnAfTScVD3qdUvc0199gmYtVotk98BIlAngDBZPPJJiq/SYUxho9HQdrs1XtkDL59JRk8GDSVRqVRsxB5ZPjQT50LWH0WRWS5LB0qCjJfPw/5gNBoZJ+3tmmezWQro2L347lgye7z7AXosJNgZ8dleUuptJ9h94MHjbRG4JhZXf47eVx+fH18IJ1ggoYJYbNmNsDOARvN2DiFChHi3OAug91YEbPPJ6HwW7tUYeMvQ1HOqAQeEydTJdj3lgDoEwCPLRCUC9+75fwDX++3QGQrNwcJAZ+psNlO32001YUF1+Gy9Uqmo2+0aQJJhk+V75QpFSigafodegWI59cKh7oCnvKdwvGKpWq1qPB5rMBioXC6r0+kY1QSQ0yXMuXpLZiwXGOruB4948zMUU1wbi51//iz+fgcUIkSIx8fZAD2UCVwtW3XvAQNf7gd/e5tbSamh3tgkkHGXy2WjL6SjwgZZ535/GFg9nU5Vr9dVqVRUq9VsBzCZTLRcLk2CKclkhvV63dwsAcB+v6/7+3vj3317P3y2p0dyuZza7bbu7+9TVg7z+dz4bO5LpVIxAPROkSiIkF16ygOwJfuv1Wp23SygkqwOUqlUNBwO7Rx3u53iOLYFigWTXcFoNLKh5gTFYb8T4fOlo5kcz6VcLpvSR1Jqt8b9CBEixLvFWQC9JFOgUByFFhiPx5axA1z+F2qc6+trSTJZJAU9uHpJBoSASLFYNOsF6UA9jEYjoz8APICu3+8bx+1dMmu1mhVdfZZ+e3trQ7232606nY42m41NVur1eilZp68/kJ0zMBsaiEXHm7GhumHxAFype0hHuwfoEUCVe+Yljdg/12o1qznA07PYehoGoMYVk3PjmfpdhX/W7ErI3kulklknE96yAhonRIgQ7xZnAfTIGsmeC4WC0R/edgADMjJEqJZqtWpNTbVaTbvdTv1+30DKc7zeSwegRx8P90wBkN/J0L29LyMLyURp+1+v1wZOr1+/Ns6eXQeLyWw204sXLwzUAEjOEXD1lg3eBoHCJzsIuHFfDIZ64j5StEZ5g9QU6gaPnnK5rGazady8dAR/b5x2emyvkIIiqlarNh2Kc6eGwDnC00N/sShJx7oIOxe/wIQIEeJxcTZAD1XgB29IR1oAbTUKDaxvx+OxoijS1dWVJOni4sIKhsj2AHo8V7xjJPp66chjUxBEEojqBoDzQO87TgFFmqDa7bba7bZNU7q8vNRkMtHbt28lyax8JaU6UKFlyMi9vv90wAoZNgsDBVqu0Wftkox2Aqj5XBQy0rEDGC9+z/8j4fRUFJk+zWFQcdQu6vW6Fbt5P/0NPth9QddJsiHmvps4RIgQ7xbhWxMiRIgQX/E4m4yeX1A3cPTIJP3MV1Q00B6+YEdxtNPpWIYKnUJGSVbsO1/pVu12uybHxEPde96jLpGOhUS6WclSyXTxrEdLzjhEPqfVaqnX60mSaffH47FKpZLx+tBC6NR9U5Mks1fA/ZOfoVHM714odHPtXBO7EV6DLoNnb7VaZk/MUHAK55KsaxYKDCqH+4pHkW+C4p76z6PewOB3Sbq7u7MZs/7ZhQgR4vFxNkDvJXSAg3QAsna7bW30gDpAX6lUzDZBOihjoGe8V/tisfgUvwu4o1ChqYemHQq+fqCGdKAToHMWi4Xu7++1Wq2Mk6cw7GfT0niFwdrl5aWq1apRKzQVJUmi4XCY4ubr9bp53QPwADp0BwVZ/5p0LIBiCUExmk5db2vgOXc08ZJsChV9COVyOaVG8nROHMe26PI6aiHuH523SF2RrW42m5R8k+fpzzFQNyFCvHucDdB7vxbp6JaYyWRsbujt7a2m06lloBT7/CSlm5sby0YpCsLv8po3zEKzjaqEYqnnsQmKmSw4ZLh4uJD9+jmt8/lcg8FAf/AHf2AGalwTHD/BZKZKpaLpdGpa/Xq9nupa5VxoJGM3hE8+x/YqFUCU+ge+PJ9lL4BJGcVP7qOXdvIZ0tE1kx0NKilqCNRgqAdwT1gEuE+co1cu4W9PfcYHLd+QAAAgAElEQVQ7g4YIEeJxcTZA70cJ+q09xVmAmM5WTMGgJ1DGvHnzRuPxWJ1Oxwy5AGzoEU/hoKWneFipVHRzc2OUCfSDdNTRM3cVIET+yLhCirE0efX7fc3nc41GIz1//lydTkdRFJm5l3RY0LrdrmX5gKY3N8N7R5J1zuIyCdVEhoydAEHTGdYNUEH+uH7Yy3K5NMknzVHsAKBu0N57uwZvxeAtFryvz3g81nA4tCIuTqQ8K28TzQ6FQv1pATdEiBCfH2cB9NKxaQrKha37drs1FQnZJ9z5er22nwEABoOB7u7utNlsdHl5aQqdSqViiwngCoCipiHjx7IX0AJ0ptOpOUxyTMCW7Huz2ZgHPDz2ZrPR/f295vO5aeWlg7qF9+L4eHFxYdkrAIr6BlCWjkCPpbP3i8FrhmNKSi2cvC+TyaQ8gLjn3o+Hmonn3Pm7p1foW4AiSpLEpnDxnHhGGKZ5SSvgTh2E3QVSTGSXgaMPEeLd42yAXjp6pnsw9o04kmwQNvI/6Wi6Jcl05Y1Gw/Tb8P6ADpku3DaWBniyoJ1ncQEkF4uFLQLValWNRsPoJpqVarWa0T3NZtPqA/DR8Oy73U43NzfmwLlcLlNadMDYDwDxzphQKaedt4Ap9BKLCn4+ACnZN4CNN42UBnpfwPXOoZPJxBap8Xis1WplPQbeoI57jf0wzxRg986ZvlbAeWcyGXMM9V76IUKEeHycDdB7Iyz07pKME4d+uby8VLvdNtfJ9Xptg7ElWRYOrQLXDnj7zkq46MlkYvp4n+VPJhPjujk2fy+VSmo0GvZnPqdUKqndbkuSLi8vjSeP49gydAALvxnORZJ1xpbLZXuf311gUkYRGsoInhw/HwrALICAKAsXSiZAGZ6cc2Ex8M1MNDRRx6DBCmonSRLzv/dOlDxHgJ7XyNzZscH912o1K1JTxIXvD52xIUK8e5wF0AM+gJlXgABaUA7NZlOtVkulUsmKmd4HnUIjckEACwCko1SSja5D2UFWyr+R9QI60CPY/6JE8QO/WQCkQ8MUXvfD4VD9ft/OCcWK93S/u7tTp9PRy5cvLXOG5wZ0/blgFUxGz7lB38xms1RTky/YojjiPntFD8GidroLAth9YZjBLTSaUW/BNdTbKnOe0G5QMyisGKLO8/e2ySGjDxHi3eMsgN77m8Nhe6073DTyQAqUSCy9fz02Ar6ACx3kbXGlA5BNJhPLRvkcryeXlKI/Go2GTY7CadMPyqhUKgbGZMHdblfX19daLpcmHQXYPHCt12vju1utlrLZrObzueI4tmvgc/xMVmwbWNSoC7AbkmTSRHZOLEjcI7J43kuxluPCj3MfsVHgOtfrtarVqsk2MalDFQUFxft5FrVaTZeXl9YT0el01Ol07NjURfDiCUAfIsS7x1kAPVQJqprNZqNWqyVJpmRZLBaq1WqmL0fLDbAAIkxmIrP3Nri8zw8GAeQ5D4qb/Kzni7fbre0oOp2OATWZKjsJP+4OCWe73dbr16+tkMy5E5wzmTUL3mKx0HA4tLqFP3dJVmTGOx41EMVPgJ7CK6+1Wi2jVlg4fUOTH86C8ondCQvr6S6q3W6bHw4NUvQA+HOvVquaTqe2oL148ULNZlOz2UyNRkPNZjO12PJer5gKESLE4+O9gT6Kou9KmkraSdomSfKtKIo6kn5D0jd0GIj8C0mSDH/QMbyczndkSodGIiiWcrlsHuqAIdJAbz0MDUOjDb9D9xBQKPi3w2GjMd/tdinLXUk20MOfA7uHU2UIgImfOzsAz01j7uWtkmezmZbLpZrNpunqs9msDWGRjgO8MSODj1+v12q1WtY0BdBzn7gvUD9QKF5GSi2DrJzr81w+0lPpqKsvl8tGI5Gh87v3qanVarYQtVottdttdbtd0+tTy+AestD753yO8RTfhRAhPkQ8VZvhv50kyU8nSfKth7//sqTfTpLkpyT99sPfQ4T4cYjwXQhxdvGhqJufl/SzD3/+u5L+iaS/8oPe7Plg6aiplw5ySgqSZOq+ucoXAvnZJEk0nU6NUsB1sVqtpjxvsD7O5XLmRQNfncvlNJvNUtQMqhc6NJFZ4qOO5h7qBrqFkX4oZaA9/AxY1C/oz+M4tglYePTzb5KsQ3g6nZq80Xvd0LSEZBLZKU1YqGB8bYN7TvdpJpNJnSO7h+VymZoCxbATSeY06RvVpKMNsyQbeMKYQp4TheLFYmH3kIYq3zn7Ixbv9F0IEeJDxFMAfSLpf4miKJH0XydJ8h1JV0mSvHn497eSrk5/KIqib0v6tiSbR+oHX3hJo5cv+i5Ob13gh4jvdjtNp1Pjx/FlqdfrKZOy3W6XGmHYbDat6adYLOr+/t7sEKSDLh5JJd2xDOjwdA2Lji+gUqSlixXKiPNGr86xx+OxWq2WfSZNRQB3HMf2OoVsX5NAZQNo+y5VXo/j2CgeFoaHZ2O/U9xGyYOXjZ93W6vV7N6uVqtPTZRiUYOmoq7A8wPMJ5OJSV2hhdDs+1rDGccX+i5I6e9DiBBPHU8B9P9WkiSvoii6lPSPoyj6Xf+PSZIkD//xdfL6dyR9R5J6vV5CJk3HKaADn830JrL55XJp4H0KLGjIUWnQXIWc0M+A9Z2YWCAg70RLz7nU63XVajXjuZmz6t0XabySZLsBagHIM8nO/ZAQsl+Kj4PBQM1mU7VaTZ1OR9PpVOv12nx14PBPTclQ3nitvr9Wr5yZTqe2MFED4J5LSi2g1A2we+A8JFmTlHTcnXE/kH3W63Ub95jNZlNzcbFEuLu702g00m63M26fTmUktqc1kzOLL/RdePg3+z78oPeECPFF472BPkmSVw+/30ZR9JuSfkbSTRRFz5IkeRNF0TNJtz/sGKhVvM7aA5QfSUdmT7aJJtvPNc1kMiYtBDzpWpX0qelVPgv281Shc/x5ATSoe6CKfIMQgOm7VTkHOjwxRfNqIVQ8g8FAg8HAXCOvrq5sQLj//EKhoHq9nrIcfngW1kxFU5M3cONeAf7etVKSKXE4V9RQ7B7iODbwlY52zFAsfDbHb7VapsCRlKLaeE5xHGsymWg6nX6qiIx8FYrsXOMpvgshQnyIeC+gj6KoKimTJMn04c//jqT/QtI/kvRnJf3Vh9//4Q87Tj6ft6yVtntABEB8+DzLSmkeoj0eQPMzSMkqPU3gpY80RPlB3PDAZLpQLNKRXmGBkY62Dew00IhzfP5OUxf8PsoVzvWUC4eDb7Vaur6+NoUOQFer1dRoNFQoFAx0aQzDpsHTSAAldJEfyYjslOtkmhX3BjoJpQ88OtdJvwCqIHxzWKz98bg+/n0+n2symajf75uKiC5YSbZDQpPPbunc4qm+CyFCfIh434z+StJvPoBJTtJ/lyTJ/xRF0T+V9A+iKPoLkj6W9As/7CDZbFZXV1dW1IOrlo7FVW/Jy2IA78vsVAL6B4mgB2LpSE14KSBgBNDDQ2ezWdsJAJDIDj9rZirad86DgihFWpqL6AsAXNH58wv9/P39vabTqYE8zpibzSbldAm4A/rQYJyjHwXINbOgUuj2vLhvbuKcoKkopHIf0dgjMeX6KBz7rF06DFlnoAiv45tz6nWErQK0mreNPrN4ku9CiBAfIt4L6JMk+VeS/o3PeL0v6ecee5xMJmODOLzHuXQEHUDXFwjJ8FFtcKwoitRsNlP+LmjITwEfJY23TMAuAcAGXMmG6bxFpYNaxg/b5tzJsAH6crms+Xxuundv3uavPUkSxXGs29tbvX37Vh999FHK6wb1jCT7XD6TpiZPy/gCd7VaVa/XUzab1Wg00nA4/BQvzrnV63Xt93uNRiO7H9QZyLr7/b6y2ayur6+NKoNy4dlRZJWkt2/f6tWrV9b8xg7HZ/0E9Be7kXMF+qf6LoQI8SHiLDpjkyRRrVYzw6zZbJb6QrOV9w1NKHCYhORtbWmtB7hRjWAV4D+3WCzalCl2DtAP7BLglv2Ow09dQglEAxeBtwx0BJy/t2A49ZenYJvP5zWfzw0UkVp624HTc+U8fBcpn0UdA/Owdrtt92U8HqcGj9AQJslGCFKEpXO41WpZrWO5XGo6neqb3/ymqWh8Zy1eP2T0r1+/1uvXr+3ZsRhg9cygGOnoL+R3UiFChHi3OAugRz8NSHu9tPeq8cZZqF5Go1EKvEulkuI4VpIkZjvMMBOKeqcgUq1WTcdNodQXJnk/x2OxaTQaRj9Q1PXqH+gY7wWP3BKdPpOx8Iopl8tqt9vGV8Nf393dfcrnhQEhKGIAWG8K5xcVFiE6eFEbMaPX0zXsmprNporFoo3+85O7vJd+vV7X8+fPrS5CbYNnxRhF6TAHlnGJqJFYtKF/kGJyT9Dl4wwaIkSIx8fZAP1wOLQsFQCQ0hw9WSpadEmWjQIM6Oq94yU7ARYJP1T81J+9XC4bZw24sOic6tQZ2u3B3r8fGmg+n5uCRZJlp6PRyPzoyahZfJCIArCDwcB86aUDuI5GI8uEWexQF7GocC4sAv7vFIm9bw3HYDGCy2dBhBpjR8V1Pnv2TM+ePbNpUbiR8gxPm7fQ5UtKFYeht7rdrqTD4oqRXLvd1osXL57qv12IED82cdbdJyFChAgR4v3jLDL63W6n0WhkCo/ZbJaau+qnEdEhCn0C7+45+uRhahI0AFp2aAJoGeagktmTtfK6dNSmcy6+gIrCRDpOTcIETJLx85iH+UEgkjQcDo26wVoYDTtySNwmR6ORaeGlowQTOsv/gnrxVBIe9N6qGKUORW1PV3Hf/E7E20fjUCkddgBXV1fqdrtKkkTVatXoKjJ7XwTnM8jieU7QPc1mM5XRt9ttVSoVXV1d6Wtf+9p7/V8LEeLHMc4C6OHboT+kY/MOryO5Q6sNRYKqBUqDbtPT5h8WBrh4ScaTj0YjK7Kilfc0C3/2lBLHpuAL3ePVMLg9AvAsZtA/6NIJeHnfqERxM47jlNIFsKbr1jdLea8ZXgfk/Zg/wJ/zoQbgewjw1/GF0kwmk7rn9Xpd7XbbVEkM8+ZzThu6kJnyPCqViqrVqi2UvV5PFxcXduxWq6VGo6Hnz5/r+fPnT/FfLkSIH6s4C6CHl+33+5pOp7q4uDBAY+gEQ0ZQj2B8hYRxPB5LOqpX6Jglk/TWxwDaZrPRZDKxQRwUJensBBzhlmneoVCZyWRM8cIiVa1WUz0AyDSpLSD7RIYICLJowbtjFQwnjrLFdwBTpJaOvQG+huE9Y7zvPIuM36F4oMe0jAWTLlfpKK30M3tbrZZqtZp54iNbxfqYhY/7j3e9f/aNRsNsHZ49e2ZcPGMjO52OLi8vbacXIkSIx8dZAD1FSOnobcPWfzQaaTweWyeod7ME+HwBlKKt16Ivl0t1u12jR9CDx3Gsfr9vVMtsNtPNzY0mk0lqvisyy8lkYl2gZLa++LhYLGxQiiRTvnigpsBJcZGZsRShb29v1Wq1bLHxnbteuskC5w3UUBpBg/gxiAA9i14cx5aBs6vgdyZCsVtBreSL29fX1/Z+mrbG47HiOLafLZfLuri4MEqOZ1QsFm3XRIdzs9nUdrtVr9fTixcvdHV18P7q9XpqNBpmgREmTIUI8e5xFkAv6VNj4rwdLzw24EGWSVelbwaC5mk0GjZ5iQy/XC5rtVqZ0oXdgrcCYLSgJHPBBLg3m42pYuCvfTv/bDZLyTFxrPSWu4BgrVZTr9ez855OpykaytNLp7YN3C8WNN9JimKGxQ+ghwdnJ7Db7dRoNAz0Twenk2kz3hGHTpqzvGMnn3d/f58aXcjOgt0C96XdbtuzYDALU6Vevnyp58+f24SxbrebGhTPswsRIsTj4yyAntmtgKDPouk6BeDILH2h0+vo6VSlyQZPFv6+Wq2M5iH79C3/fig4PixQN3DkWPGSoS6XS3OuZOHhXBjD5z1a0OAjvZQOuwnf7MTOxtssUDTlPAFPbxlBtiylewDgxfHzyeVyurq6svvqC8WlUslM3bbbrdE41AKQebILw/SMRZZCMgsWA765LwxWR/LJ51UqFX3961/X5eWlnXetVtNms7Hh6Tc3N+/3ny1EiB/DOAug32w2GgwGVmSFOpBkyg4yeUlmLAbY+eEgZI94xcPn39/fW+OU92CBEgFcfUF2Pp+bJ4t0yEThtE+LtShcOB/pWAvAsOy0IWi/3+vu7s6O4/1dqA+w09hut0aRcGzOF30/yiHO4bN84XkfOwooJzqTpQMQd7td1et1bTYb6wxmcfOfJR2tiVlAKK7i/Mn5cJyrqyvz9ffDTdrttj766KOUame9Xms8Huv29lavXr3S69evn+q/XYgQPzZxFkAPMCPx89llvV5Xs9k0YPeUhm+PPwVJ32SFxw22CL7Rh4EbqHkKhYKB1nQ61XA4tEIkU6R856mfjAUP7huF8LkvFosaj8f2737KFNfDIsP1YJFA85PnuXk/1I6XRDKIBN6fa/Xn5i2Y8/m8FYmlA13S7XZtIhYZPbQNx+ZY3jSuWq2qXq9btzHPlp2BdFgw6/W61SqonVxfX9sAExbAxWKhfr+vm5sb3dzcBOomRIgvEGcB9BTk0LSv12tzjDzVwiPzQ4MNBUL2jKLFd9byi8zb2whTtF2tVsbnYz2A/JGFgWJspVKxYdZIFBeLhdFPfnweuw9qBlgieKMz3kstADBFQdRsNlPDPLgvFEa5H2TGfpfiF0Zkk9vtVq1Wy64ZJ0x48YuLC7N/ZmCLB3bC2zcjlWw0Gmq1WjY1jBrCbrczxQxjFVlMGLZC38CbN2/0u797mNkxGAw0Ho9TNg8hQoR4tzgboKf5yHuySEdDM8DCc+nMfPU/Q2YL5UGTEYNCPHBTxAR8vYzS881k7Pws4IevjiRrXoK/lw5ASMERrp6hHVEUqVqtmhtlu91OjeeDtpFktgwsBJIMTL3+31sJSEpZBTMvlxpEo9GwSVHL5VJXV1fq9Xp2bG/yVqvVtFwujWrxixP3BZ691Wqp0+nYzoisnUYqSWbRUK1WVSwWrRBMv8D3vvc9/d7v/Z4k6fb21u6pn2sbIkSIx8dZAL2kFA3gQQ7DM0lW1CTrXq/X9ndPn6BRp1jpTbwAOimd7XuahEyUph8+n89hgcCmeL/fazgcfqr7NZ/PW+bMokNxUpI6nY6BKzTLeDxOzY9lEWSgNwsBjpveZAwQ5nWMwiTZwBN2EpVKRVEU2ef1ej2jVlgoGdhSLpdTxV92DoAuSqFWq6V2u22zd+HlG41GSk/P9UnH4d/j8ViZTEaDwUAff/yxvvvd70qSbm5ulMvl1Ol0UjMAQoQI8fgIXjchQoQI8RWPs8jooQg+a/AE3aubzUaXl5cmt0NHD3VDwCsjm0QG6GV+3o2SQidFTfjmTqdjWbJvSoKPn06n1qmLN321WtXNzY3x6JPJxJqIKPbi4lgsFk1SyLEnk4lJQyn4epqJ85dk2bPP6Ll25sl6Xx4ao9D7f/LJJ9put5rNZtaV6juGvVe+JCvAlstltVqtT/nZsHuhuYmuZLJ/LJslWVcuvQMUWReLhe7u7vTJJ5/o7du3kg5DTRiC7ovuIUKEeHycBdBDj/juTT95CRtg+GwUKN5imC09wzkAZZQ5o9HIbA38NCp4ZkAfmR8NPJ4uQRWEphsaA7DvdDr65JNPUj49o9FIk8nEfHK4tlwul5oBSxET+qhSqVhz193dnXnP+47hWq1mlMjpwuDtmCXZe+M41nQ61Xe/+12Tba7Xa93e3mo4HEo6jv7zNs70K3gTMigong98fBRF1uOw2WysRsICSGPVZDLRarXS27dvTeGEwoZ6Cc8JnyNfvwkRIsTj4gsDfRRF/5qk33Av/YSk/1RSS9JflHT38PqvJEnyWz/sWHDnzAUlG5eUUo148zB4ZIDOT15iWAWFUyR+NOf4Oa2ofciE+fn1ep1678M1m/XAfD7XeDy2IisKIA+AFFCn06niOLZFqFKpKJfLmU89x2b34Yur+/1er1+/tmYv6gWDwUC9Xs/uAT/HzojJWfDi8OaLxUKj0ci87tkN3Nzc6OOPP5Z0WBRoFqMWwSjH7XZrdgUsDByPLtvlcqn5fK7BYKBsNqtWq6VcLmeF67u7O223W2vI6vf7Zto2GAw0GAzs+XNdfuzgOcZTfh9ChHjq+MJAnyTJ70n6aUmKoigr6ZWk35T05yT9jSRJ/tpjj+UHguBzAyhIMumeV8B4CwJvE0DxsFQqGV0zHo+1Wq0MtHyjD6PuUO74eal+hyEd57r6wSIAIMXbcrlsk5egSsbjsTlkUljN5/MajUZGl6D1x7+GzDhJEqMvOLfT93uAZ9QfpmYsVCwus9nMvGe89fFoNNL3v/99SYddURzHGgwGJnWl54Djl8tl8+m5u7sz64jVaqVCoaDZbKbb21vtdjtdXFwol8uZlcX9/X1qcAs2E9hVx3GckqhSiKZ7+BzjKb8PIUI8dTwVdfNzkv6/JEk+9p2Yj41MJmN2tPl8XnEcW1aMCiaTyVgTDhQAVIrnf+GF2+22drud7u/vzbYAfb33nmGHgJ0uIIWk08v5MFCjK7RWq6X83GkWok2fDBRagmtkdzIcDu069/u94jg21U61WrVzxWDNe+n75iXsF6DAvBTRWyAArqhycAD19I10yKLjOLauYGoR1EegyqCRJpOJ7Q46nY7RTjc3N6lB5QA9ds2z2SzVUMXuBxdQSea3g5/Qjwh1817fhxAhnjqeCuj/lKRfd3//pSiK/oykfybpLydJMvxhP8z2Hv11Nps1WsDbAjOoGmCTjp4vbOnJZqEFyJqx/PXzaHGIRN9Oho9rJD/jx/exY2g0Gur1erZYUAjlGji+JKMx6Dit1+tm0EVWzEhCgN5r76GQ6MqVZLsC748DWDYaDWuQ8rsRgsWK62NBYNGh+Ws0Gmk+n2s4HKpYLFqzGsNEODa7FiymeU7ce+gh33PQbDbN6gLZpu9x8AssIxl/hID+vb4PIUI8dbw30EdRVJD070v6Tx5e+jVJvyopefj9r0v685/xc9+W9G3pUIgEyAA1b/GLnhw+3VMVgDxABg8uHYGc98JPA9xk0TT00BlbLpdNOSIpNTHKT4AiM+bv2WxWX//61/XJJ59IOnrdwGePRiO9ePHCqKMkSVI2xdgMcI3FYtG6SHO5nOr1ulE3dBCvViu7Hjpp0akznYrPojGMP/udSbVatcXyNHv2bp50J6OTl452z4PBwAae04PgPfU5PsfAy5/6iO9rIBaLRcpD6NyB/im+DyFCPHU8RUb/JyT9TpIkN5LE75IURdHfkvQ/ftYPJUnyHUnfkaTnz58nnl7xlIn/0mNh8PDzn9kxyyKBRwqdn2z/d7tdapA44MhOgQYigIqmISndXcooPu89U6vV9I1vfEPf+973JMn8cpIkMaklWbPnniWZl70HSEzC8KihaUg6dIziwknBEmqJ++N3OgArdAndsewyvDUyAQCv12urLfAcyPAlWfF1tVrp/v5e2+1W7XbbbB2offhRgpwP/kKYsfFMT101UT/5iVxnGu/9fYiiKGhIQzxpPAXQ/6LcNjWKomdJkrx5+OuflPQvHnMQ6AL+7CcmAVoUKvmysyD4UXrlctkcMBlagiwPrpsMHbqFAisaebJ23w0qHRYG2vUnk4nu7+/NsAxzrl6vZ3NNKQKzsyCrR4qITYAkMyJrNpum7EFiSMbuHSPhvOHPqVf4qVs+AwbksVSWDp4219fXarVaqX4BuosBX9RJ/j54N1GiUCiYwohagLddIFjMsHGuVqs2SpAsnwUKoPfuomceT/J9CBHiKeO9gD6KoqqkPybpL7mX/8soin5ah63qd0/+7TMDSwNklhTnJBkfz2t+m8+fvf8Kem0AlkyQLBaLAEnm3uhBBNoE4PPHjqLI6JDxeKwoitRut9VqtWxRYIi1JL1+/Vr9ft8AdjQa6e7uTsPh0Cgqju3PA9+f6XSqfD6vWq1m0kqvulkul6rVapb1kzWTyXuZKnJK7mMul1O73Vav17MhIIRX61AY9Z8BteUN1qilMHDc90LAsRPQXCwg7XZb1Wo1VZ9gpwO1Q23knIubT/V9CBHiqeO9gD5Jkpmk7slrf/qLHAvFCNt5OHcaphgSwgg+b5TldwC73c78XLwWHe66XC6bMyYSTF+09Ja7UnrMIRQEHjJQGDRyQS1w/GazaZ8BLXR7e6v7+3ubnOSHd6C7R7UC+PZ6Pd3c3JiLo6TUbFboKvTu0Fr8mXP3/v2FQsHsgqF9fNepn2qFjzxNaMhI4e5LpZI5U6Kdh1f3HvZk49hOQ5E1m82Uy+Vms0k1qeEYyq7pXOMpvw8hQjxlnP0+OESIECFCvF+chQWCdLT0JVP2w0EkWWON90AnI2RuqyQbwF2v11Wv161oimMjNIF07EaF0vB0Bxk9BVJJZi/s3SWhGPCB9+fIcI1CoaB6vW5doG/fvtX19bXa7bYuLy/t2Pf392bTQKF2tVqp1+sZv+93GgwEgZrCV4Y+BM+Nk8XzflwlsU3AH4j7QsaNasYP9m42m6l6QbPZtNoI/jU0uEG34JMjSV/72tdS4w/pN0BeyvOSZNQY+vwQIUK8e5wN0GO6Bc3hAQ1VBjbFvlsVAGOrT1GT9wE4GIjhIy8pVQfwRUpvY+w5ej8mEErCUz1450PH1Go1882BUlqv1zb7lH+XpOfPn5uaB54eCSXzXv050znrh5HwuXSaou+Xjl4+UERQWL6RCqrI0yN+qEmxWDSbiv1+b8DLIse9nE6n1mDFtUsyeuejjz6SJAP12Wym2WxmKimuWTqYt11fX39mLSFEiBCPi7MAem834HXz0nHCFF2t+KgDbrTck0Xj0YJCh+y0VCpZlo96xA8gR6Xih2/zmd698lRBQoZMExEWAdKhP+Dy8lL9ft/OAzXQq1ev1O12LaPvdruaTqd68+aNSUDpHvWzZjmXUqlkBWy/MKG+oRDNuaBM2u/35kDJaEUaoZBr+vvvh4Ez1BvZJEBPrT34U7EAACAASURBVIEaxv39vTabjRqNhi4uLsymotPp2LVy3qh07u7udHt7a5297Lrw7O/1espms2GUYIgQXyDOCugBdDpTJVl3qG+Y8g1S3ttFknW2oo+nIEjBsFarpTT6gL53u4TG8HYJHPt0eAn6fGid1Wplxct6va7Ly0vd3d2ZZBHnxtevX6vX61mWi20xg1RYnPb7vQaDge1KuO5yuWzA7cEdSSN0Ftk0PkJQLs1m0/xz6DPwhVuvOuLaq9Wqne9isbBdRqPRsGybXdR8Prcdy2KxMFsKninF8e12qziOU/Ng2+22vZc/e+VRiBAh3i3OAuhRxcCHkx1LBwBE3oc7ojcgo9HKD70GcAF8WvzJPD14M84OwAfokVl6WgLVh1cFYQDGIiApBa6dTkfdbtfoKDLt+/t7ffLJJ+p2DyKNTqejWq2mTqdjNQDOazAY6OrqKkWDsChhHYxOv1wu2y4Ajp9zAuwBejJs7AughdjNeOdO6Tin1nsN8Tr3nzoIVI8fjk5A7bBgLxYL86Ov1+vqdDp6+fKl3Rd2C/QuhAgR4t3iLIBeSoOLBw6vDQcsAW4y9vl8bkCCNp3F4FRi6LtHsSRutVo22CNJEsVxbGCFTl86LEj41MC5n1oMMANVkjUNNZtN3d7eWnGyVCppPp/r/v7ejMQqlYqq1aq63a4Vo6FZoKK8NQQZMYuOt0Jm2InP6GezmbrdbmpeK35Aft4tx6bfwEs2eQ7w/5yL1+jjX8+iCBXjJZOYqVFLYBhKuVzW9fW1vvGNb1jTGQshbpre1TREiBCPi7MAerxrABQ/F9Q3z5DNwxv74qlXyQBOpy3zcNoAd71eV61WU7fbVRRF5tcyHo+N4imVSvb+bDZr/PJutzOFCX9nIYDrppu0Wq1aJygAysKCedvl5aUajYaazaYN5ADcc7mceb54L30yeoZ4cEz84ble6UBxUYBlAfIDWKCuJBllxb2M49gWlfl8bt29vhnLny9Fc+ngbEnhmh1DHMdWr5hOp+a/0+v19BM/8RP6yZ/8ST179kzSoYCL4qjf76cGpIcIEeJxcRZAT9ZORnnqb0OjD4M6KAIC+kgBpWM2ig2C79KEuwa4KXh2Oh0b5Ud2SpHUHxvaB9oBkKaQy2dj5UAzkOfTpeNkq+VyaaZm0+nUmqignrg+qBl2PZwXw1UAXz8UBWdIaCr+LYoiG0iCLNMPIed64OepE5CVj8djDQYDZTIZ27lQDyCTx70Su2OeKUDPkBIsnHEQffnypb75zW/qxYsXarVadq9w/xyNRmdvahYixDnGWQC9lDYp80DvvW7wXUEJQzG10Wik5p1CoXiag8zWZ4S1Wk2tVivFIWOZMJvNUsod6UD1kI1Pp1Nzk2Qxol4AuI3HY6OH6vW6RqORWTrAw3M+w+FQrVZLzWYzRXOQ0eN9w3VwTBYvPHgAdH4B9N4vhzrGdDo1Ssy/h90Q9xsnz81moziONR6PVa1WDegZlcgijO4euSnFXc4dOSvyyv1+r1arpZcvX+rZs2dqNBq2GMdxbBl9yOZDhPhicVZAD33jJ0mRbUJ7eCUMniw0RklHoEYaiQqFwipFPUkpnTfgJsmUL2TqfuFB5ggN4n3xpXTmCpdfr9dNZggAwrEDlre3tyoUCqaL99k8fDdzY6W0vJJfnmcH8LlXUFDcy81mk1p4vEyTjJvPqFarih7mzVI0RVnD+zebjd1r71njn5+nhmiOQ+uPXp5ZvVg9vH79Wre3t7YgnBaIQ4QI8fkRLBBChAgR4iseZ5HR+0YkT9FIxw5UaBrkjp4T5xj87gde1Ot1o4O8ekc6UA5xHBvd4kfYUdAdDodGAXmTtHw+r3q9bnUDb53A8WezmSlMer2eJpOJSQ7pTmUnghkYpmi+aMyw7lMLAIrX3Jv5fJ7izbFTkI7WwN4H/u7uznYZmMdx3nj1r9drs3GgNgFVBF3Gc5FkzpkUzKOH+bfMGeA9fqxjPp9Xr9dTq9VSkiQ2IFySXr16Zdw8fQAhQoR4tzgLoPdNSFAfvnAJ2AMMHqyZDOX5WzhtSdZs5VUoXokyHA61Wq1MtQJNga3CaDQyugDKhoWoVquZ3BNNv9fYM8EKDrrZbFpPAA1HnOd6vdZgMEiph3xX6mazMSUL1w1g+/oEckYWT6ger5GfTCaKokhv3761CVWeiqGwSpEVS2WAGzD32nnfeMUCwRxdhqxwDtBfWErn83kbqrJarXR3d6c3bw4W7jc3N58aExkiRIh3i7MAehQ3FPG8FI/M3Xez4ksD6IzHY3u/H6gtHZt5kCzyGp8L0ANyfigJwA24sGggAa3X67Z4sItAZy4dwBslT7fbtQEbZLfUAiSZJh+v+lqtZrUKsnsP9OjPqS1QDI7j2Hx+JKVUKlzHaDSyYeDr9do09SwKXAu7EfT7LGrUGbyvP4VX71cEmPueB+m4eNM3USqV1Gg0FEWRJpOJbm9vbcD6ZDKxHcNpp3KIECEeF2fxrQEckSH6wdPr9do6WlGKoBaBjvADOWivp7OVLBRqguyUz0ULD53AwG2ASzo6aEKfeP8dPx7Qe6pLMjliv99Xu922zlKuBV93STYQHXknRmJ+IfH9BahW0KCzC/LX7ncM5XLZfOBHo5GGw6H6/X6KNgPouf+YsAGyFG1ZjDn3SqVijVecC0NUcLXEmZLjM/wEr3kGmXBeKJd4zkhbvWd+iBAhHhdnA/Tr9dqUIZ579zRJqVTSdDpNcdPw06cZP+FBjEEhAJSfZAUHDcCh8vHgynu9Jw86do6Nuodz32w26vf7urq60n6/N7kliwJDStDOIyMsl8tmD0CzlG/eiqJI8/k8taAVi0VdXFwol8vZxCcWK8zBJKnf7+uTTz4xDxpqESxQeAoxC7bRaNguablc2r05tXyQlFqkkb6yY/KNZOwWGCjCnF+eE0F3LjsKridEiBCPj7MAegJg9dbA2P6ieb+7uzM+3Y+2A+jpDoVygPNnrqqfOQp4+m7c+XxumSvFP7+7QG7p7Qf8wG9fdGSRodkHTXq73dZgMEjJHwE8zt933AKufgGEtqJQCn3DjgWgB4w7nY6azabRK4PBwICWIqt3DPXjCOH1acSiI5fdQqlUMgkouwga2Sgse+9+P4uX3QL3F4tmX3jm/4Wvr4QIEeLx8Sigj6Lob0v69yTdJknyrz+81pH0G5K+ocMszF9IkmQYHdDib0r6dyXNJf2HSZL8zud9BooWCnR+BixWArVazX7RJQr4ebBYrVZmRwC4UzAEwKQDJcPwDo4F4PnmLD9gez6f2/kAWN7W1+8CoCiGw6EGg4ENz+h0OppMJqkCMnw153Dqjgkl4z3gy+Wyut2uKpWKdQHD73OO3BdqA76AC0Az9IXMnOvxQ8ehlMio/fuxPebzuG8Ym1Fw5ZpGo5EVtb2vP8+Uhi5JKeuHc+Do/zC+CyFCPHU8Vkf/dyT98ZPXflnSbydJ8lOSfvvh75L0JyT91MOvb0v6tc89CVeEJYvmS+0VGwCyL2p62kA6SjUp1nIs1B+r1coGXXgjNdwxvcPlaSaJZQEKHhw04d1PrwEPn+l0quFwaF4wNAdx/n5wOda9KJDInMmmeT9DyF+8eKEXL17o2bNn5vTI9Ch2Qa1WyzJtaCYWBO4LgO6bnLwvz3K51GKxMCUOk7Levn2rN2/eqN/v2y6IxZmf5TXuI3JJ7iELIll+tVo1vxwWUorfmJx9ifF39AG/CyFCfIh4VHqUJMn/GkXRN05e/nlJP/vw578r6Z9I+isPr/+95LDH/t+jKGpFUfQsSZI3j/gcy1y9amaz2WgymRhQA4iSjKf2g0j85CjeR+YINSMd9OksFFAyuE1Op1NTjUA5AJS+UFgoFNRoNFLDP3x9AYkj1AeL1+XlZYoXB+grlYqm06lltBQ/AWCULhcXF3r27JkNSsczpt/vm7sn/DbHx05gu92q2Wyq3W6rUqmkFkVJqQWL6/C+ONBV+PTQCYvHDUoaitWSUpOwZrOZDSWHeqKA60cy8nxx/Gw2m6mayZcRf1jfhRAhnjLeZx985f7DvpV09fDnF5K+7973ycNrP/A/N0oNtNdeiw4dANiiSEE5AyB62wGoHxwWvcfKfD436oZWe5qpyCbxzqFA6wGQRQg+md2AV68A9Cw6cNzj8dj8YMi6vekX3LRvOMLPHdqm0WhIkk1dAhixR0amimrHAyMKIYzNvLlZkiR2bMDfWyv4Bi6ukWdAxo8FNHWWzWajV69eKZPJpJQ0yE5ZHLLZbOq5eV8citz1et0M4s4wnuy7ECLEh4gnITyTJEmiKHqnKlkURd/WYTubUpOg3wbY4aSTJNFkMrF5olAjgBrg7QHHAy2qjtlsZosIg8G9YsfPR4U68UAvHbs/2QV4b57lcmlZNAO7qRvQcUrHbrvdTnnlAPQYhPGZFD+xVZYOvHin00mZl7VaLY3HY7tHdOFy/7hGVEqFQiHVfMZUJzzw6frlfnmljc+uGeeIZBJP+vV6rZubG+sP6Pf7kmQNVWT/1AgoFPth5PD/jUbDaLxzji/yXZDS34cQIZ463sfr5iaKomeS9PD77cPrryS9dO/76OG1VCRJ8p0kSb6VJMm3vuzteIgQ7xnv9V2Q0t+HD3qmIX4s433So38k6c9K+qsPv/9D9/ovRVH09yX9EUnjz+MkUXigdiErl47ZPoVNAjrB+89ISo0P5BgU+rBK4Dj4oaNQ8cVbrBa8746nMrzeG5knhUd49CRJzDtHktkfw1+3Wi1rIkJZU61WtVgsUtQNdAjdstKx6EwjEteMigjli28OgzuHYmLXw78xWhClDCMST71ruEfsLlAq0URFkxR9AXd3dymbYXYU9BTgq889ZNchHSZMdTqdVO/DGcaTfRdChPgQ8Vh55a/rUGy6iKLoE0n/mQ7/qf9BFEV/QdLHkn7h4e2/pYOc7Pd1kJT9ucd8hud/seiVZAqWU3kdqg3PE/ufhzpggVgsFmbE5RcFOHDvv8L5wFNTB4BWgBJCHojEE5UJx0C+CZWBgoYFgTGGD/fYBnBwPp7CqNVqNjtVOlgD3N3daTweazwem0+Pl0hKR+prs9mYhz0qF7T3cPZQN76TlQUDDh0/ez/u0dcnkL/u93uzQaY+QVQqFas3NJtNM56TZAsuCyTndVpI/7LiD+O7ECLEU8djVTe/+AP+6ec+472JpP/oXU7Cc98EPDeFOT8Ag3mo2+3WVDEAGwBEIZLiLk1IZKbScXoVmSXZZaFQUBzHxn+fAr3nu8lk4enJdiWZnw0LlNfvo2OnAMoCVC6XVa/XbWFjp8OQE2oXSB1Ho5HJFekv4Br5DD6Tz8Pm4fb21uoInU5H9XrdzgW/fRYofrEwMgdAkjVsDQYDNRoNtVotrVYrjcfjVEGbLL3RaKjdbqvb7ardbqvRaKR6Gby6qNlsqlgsarFYaDKZfOkTpj70dyFEiA8RZ1HZYsuOdNLLAiWZkqTb7VqmjQcOhVh2AGSHvqjqC5ZosyUZTUAHJ6ZlxWJR1WrVaCDfdo/qBs03GnQAGc8W6TjqkPcClFEUabFYqFqtqtlsSpLRLKPRyBY2Fiokmfv93szXWOzG47E1Mt3f3yuTyaSULP4e+0LsZrPR/f299Rg8e/bMsn/myzYaDfMBQpnjz4fdSLVa1XK5VC6XU6PRsGwc+SRNXYB3u93W1dWVer2eKWlYiPAZYldUq9WsiA69FSJEiHeLswB6Mj6yOe9vDsXgqRjf7s97yLq9kyKLBtQD/0ZWiJyTSVU0MPFzWCh4WoHPg7tnB4F+3o/YI3P3g8GhTUajkXnBSLIdBTQVZmZkwkg7ORd07KhjoIRKpZL55NDc5O8j1+cHgDebTV1dXRlA+w5czNNoCKOJjPPl3OH/4efZ6dAz4N0uW62Wnj17pm63q1KpZIur/zk/F4Drg0oLESLEu8VZAL33SCHzBLihdaApsEiQjuPyTgeVkMUDrj67pwgpHQeJA57SsckK/T7AI8nOkUKwpBTFAs/vvfJ9AZfrWq1WGgwGqay40+mo3W4rm83q/v5e/X7fdjK4bzI0hPsxnU5tcIq3EGaB4XO4V1AfADM7nGazqW63a9e0Wq2stpDJZLRcLs0MjkWHY/D8kHdS3KXHoFwum58OQN/pdHRxcWGFY67TG8wB9NRAqL34ZxUiRIjHxVkAfRRF5odO9u45dyiLfr9vCg2KrGSphO9QZbEA8L2SRjouIn6YNr4sfpoR7+Nc2IGcerCwMHiOHpXKaDQyXT6gXalU9OzZM0kHioIsGlD2Hbl+EpYk69Cl9gBNQobMvWGAB4sE58nCBNcO/SLJdjLsYND/44uDBYPv3mXBuL+/t0Y1hriQrQP0l5eXptih1lAoFGxWrdf/s9AB9l92MTZEiB/FOAugl2RNQdAiftISmfR4PLaBFoCQd5qUjkNMougwAHu5XBrtARCTOaKC4X382ynQEH634bNn77oJhUKwYPjFi3McjUbW6u8bpvCpXywWlskyTBs5JiP92F1gn+CdJyXZ+2ezmUki8ZYHvMneAW4KqNGDMyigzkJCYxTXQ4bP+9hlkfWzcAL0mJbRqTydTs0ugfdCaXmPHD8xK0SIEI+PswB6r2LxgEhApcCpkyF63pkAGODyAUGKfd4SgQlNcPrw95VKxRYFQJrz43wptLK78FOUvMSR7FlSqgi82+00Go10e3voren1egbAAKyfKcsCwrG89BKQRwXDSL/1em0LyWg0Mktm7hPF7FOfdxYw7hWLAI6Z3CcWTIzd+DmAngXLdxxzfBYo+ibonqUOQJGa4TIsXixcIUKEeHycDdB7IPN+MR78PS0jHbX0UCQEx/EDuwFDX4xFESMdDchWq5Vlp/l8XpVKxUDQAz3ng7QT0PfmXWjwTxceeP/lcmkj86rVqkkjmePqG5JYWE4HobDDQA5J1oulgFfpeKfIUqmkbrerzWaj6XSqwWCQau4C/FG6AMr+WgkWHb87ohZQrVatwA6n72sP1FAYpYh/P4tWu93WfD63xYCFK0SIEI+PswB66dgZ6uV7kozTBli8Qsfr3L2kkeyTJh5oFsCULNm7QgJkm81Gs9nMHBMlpY4NqLFgoOcnMDwjvO0vuwZURbPZzDL65MEG2cst4bh9DYA4LSyzOPlpWihvJKUWRO4b7p1cw0cffWT3hZ+fTqf2c1wbBWy/8LHgsbgwi5Zis58CtlgsTAaLlBab49OB7H7oCROvQoQI8W7xPl43IUKECBHiRyDOIqNPksTmtDJcgmwRbxpoDSgAFBjw4GTRvjFKOjZjwSOjUJGO1AcZN4VMPgv6hmNT0ER9Q4GUzBqpIDsAz39TYObYNAmNRiNJR3vljz76SJnMcUYuA1agZ4hSqZSyJWg0Glb4ZCfjd0Jk8QSqH6it1WplVBHXh1KHDmRv0cz1SYfdwv39vfL5vNFGWCj4/gGiUCiYFTI+O/RI8H/BnzfcPLWHECFCvFucBdBLaSmiL0LSzFSpVLTdbk0KCPhgQQyIAW5IBKF6UO8A5NLRRpjFg+lSUArovClgMr+W1wB5r7KhQ1RSqpDr+XpfbIWKgHuGLlosFmq1Wmo2mzYJCqsA4u7uTo1Gw/h86hDQWnEc27nxGvd4tVrZfaO2wPQm7jmSSJ4NtBCLqae2RqORPaNTKaR/DpJS95ln7zudZ7OZeeP4YnkA+hAhvlicBdCTwfquT4Ce0Xv4v8CdAwwAp/eUB1Clo6KHrNirYrAsAHjgxPGIgdtH6sdEKI6PdNODkO/YpUsUOwLUP0guK5WKAf16vVYcx5bhMzGqXC6r1+up0WiYuyT3iEUPUPaSSbzbfQHb1yHiODbPfelQrL2/v7d71mg01O12zU6BIiu7Fj9LN0kSsyhgUWUH5e8/fQLlctkWGOwN/ILEvZCOu6LToS4hQoR4fJzFtwYfG7Th4/HYwBiXQ69rJ1MmM4YOkWQWwKcdlTRL+QImfi40PTH7lfegF0cWCFCR3bLQzOdzm5gElcTxsVIYDAa2e6AwifxQkqlO4jhOKXTwoOE4XCfAj+YfSwSK2V4XL8mu33cFs5BwHcPhUNJRtYTpWDabtQlRPCMWFu4HVA2maaiA/CAR7l+9XtebN28sc2cB9V3HXpJ66lUUIkSId4uzAfp2u21Dt4fDoYGrJAM5qAFvsYt8D5UGYIDMEGoF6wHvudJoNFJ0CFYMZOaYkQFQ3s3RT62ivnDaUQvHjqcM0ki04nSa8tksGkyZomGqWCya3pzdQrVa1cXFhV3/eDxO0VmSUouOt19G0ZPL5VSv140v92MNkXQiuRyPx9aZ6xU2HBvuPpvNqtVqmUkcaqparZZarGezmQaDgW5vb3V3d2efx26KZ+RtLgqFglkphwgR4vFxFkCPBUIURakBFZLMLoAtPMW503mwZHpw7dPpVLPZLNU1S5EP8IP6mE6nphGXjsOwOT4AXS6XjYIB1Bn7B1WBnFCSNQlFUWQLipckepsGKA+uFW8eCpnT6VTL5dL05eVyWd1u91NunAxcYSfjh6DQBJbP5832uN1u2z0A6NnVIM2ELmOXxDPg32lgY5HyYwbjONZutzMXSp73eDzW7e2tXr16pbu7O7NRqNVq6vV6tqDd3t7aYlEoFNTr9d77/1uIED9ucRZALx2He0O7EPC1ZJP8fTabpbJuAI3uUrJDgH+322k+n6e8aPDLAdgZnO2VNwCbD4qVZOmAMTyzn2CFhXKz2bRiKNm5L+R6EzVsB1qtlnK5nObzuW5vb7XZbGwhefHihQGv9+pH+87rACY1CuoZLDytVsvoG+6Lt3eez+dW7KVYy4KIHQE7CegcVDdQWtg7+K7e29tbvX37Vm/fvjWg73Q66na7evbsmZ03RVkWJ1+nCBEixOPiLICeYh5yx3K5nBpqTVckjThxHBtd0ul0UiP2AG1JVryF+pCOHL50VHv4sXx0oLIgAMy8n6yZxQX+3C8KnPt0OjVu2quA+FyvRMEZ0g/2oEA9HA719u1bTSYTa6iCUmKBwMeGgij/DrjC3XMO7XZbSXIYOI4zpV8sObfNZmO0WbfbtWMUCgXj9LlmFp7lcqnRaGQ2yQwC94PQb25udH9/r9FoZDsvmr6YKCUdTc0wTfPduyFChHhcnAXQSzKgZ8oSYIy6g6IcCpfFYpFyXyQWi4WBVLlcVrVaNfoEsCBznc1m5piIQ+Z6vTaghc/2GSwFTYAYWwEWBD+TluuRjrYJAC2ZtZ+kBdhCr1AD2Gw2xmX3+31Jh0Xs6urKrhH1DcobJlUB3mTpXkmEwyQ1DUCUXQ4LHvRVo9GwhXIwGKRUUl4N5Rdmntl+v085b97e/v/tnc+LZFcVxz+HgSqG6tevO8YJ4jiawLiIQUSDuBBUBJmIMoIudGP8ASF/gUQiBNy6E0SYRZgIEl2pcZcxm7gZcII/EtGYdBC0J9M9do9TNVNN/7COi3rf27c70/Q4Xd2v+no+UFTV66o3996ac+655557znJypWlfQysH1c3VuGhPQRu9QRD8b+yr6M3sWeALwLK7P9Jc+wHwRWADWAC+6e7/NrMPAH8BXm++ftndn9zv33D3dAxfAp9HV+T+bCl8RYbIss5TDytmHkiRLfJXz8/PJ794XsBE4YOyTPPqTnIrSYlLCcuNkidWU+QLbLuRtBkrBapoILkjYGwVa/NW1r7SFSsBmsIf1c+trS3quk730qpC+dzzduURQArBVMnAvOAJjBVxfkBN1nWn02F2dpaqqnacXVC1rJMnT3Ljxo20YStrXnl3NMEOBgMGgwGbm5vpPEFVVdR1TV3XKc5f/ex2u1RVRVVVaWzb4ijkIQgmzd2kQLgInNt17RLwiLt/GPgb8N3sbwvu/pHmEf+pg9K4SMhDcMzY16J395cbyyS/9mL29jLwlYM0Is/zLrdLHl4pF0i3201pahWemLtBdH00GjE7O5siYbrdbspK2ev1kpUr14lcDhsbGyk3e765KJ9/7tKQa2g0GqXj+coEKbQBqRzzeVI0RZnIytVGrUI5Nzc3U+imXEK3b9/ekdQrT90sK1gbsHNzcyneH7ZXNuqXLHWNj7untssvr7HJw0XVvjxLp04sa3NWoZdKhLa2tsZwONxRLWx9fT21U8XCe70eVVWlsxRAOpSmvYi2LfqjkIcgmDST8NF/C/h59v5BM/s90Ae+5+6/3e8GueLRoaA8FbEO0ijcUBEivV4vhRHKd6sKS1IKuu+d/OYnTpxIcdk6pKXvSAkrdbHamaNcMXJPjEajHa4bKVqFhcp9IaWlvPdAUuoKJ9VD8eWaCKVcB4NBOm+gjV5F6sjXnkcM9Xo9ZmZmkqLXWOn1nUosKs5fYynXUqfTSe4d2E4lcf369R2fVzrj3eOnTXeFS54+fTrF3QOsrKykvQhtuuuex6CU4IHlIQgmzYEUvZk9DWwBP20uvQ2ccfcVM/sY8Esz+5C79+/w3SeAJ2Ccc1xFQXJLGrZzzsvKtKY0nRS9fPZSmFLO+QlUKTHVWc1LA9Z1ncr8aeNPysqailOaJBSimMfXa+WRK+e8IIhWGLL4tSmslAv5Zqw2XhUeqjMAan+eqjjPZ6OiJ8qLk+ft0YSp/uUHtxRCqXJ++ZhrMtKGt56lwFVUXJ/XiVZNynllLI2JNrX1G/Z6Pebn5zl16lTq28rKCteuXUuKfjgcUtd16osmjWlkUvIQBJPmnhW9mX2D8abUZ72RYHdfB9ab16+Y2QLwQeDK7u+7+wXgAsCZM2dcClGWW650VEtWJzBlDasoiCYB2K6OlB9O0slYRYHkG7Vzc3P0+/2kSPWsfztfAcgCV9vyqBy5frQ6AZJFrJO8ctsoLFPx8kCK+ddGs6z4PJ+PJiEgTUQbGxv0+/3UxrquU06bvMiKlG1eAEQncVdXV9NZBdhOzCaFL9eYQidzd5buo1w3/X4/ubLW1taS6yWviKXVQVVVzMzM7Dj5u7y8zNWrUdkVTgAAA7hJREFUV5MbSS4ujWO//w4dORVMUh7MzHf/PQgOwj0pejM7B3wH+JS7D7Pr7wZW3f0/ZvYQcBZ4a7/75YpJeWnyU6pyYdy6dYvhcLgjgkSRJXlhEr3W5JHnrtGqAUjRJ7BdrlCWr+6pVMj553UvuVPkmpFi1P0VzqiQQ/m7cytZk4KscPm28zJ+uTUst5Cua7WQx7YrNHF37VitfDShbW1tcfPmTZaWllhdXd1h/St/Tq6gNaF5k15Zk4vSVihvTb/fT3sK2vfYnb1SriGlmFYGzMXFxVR1C0iTQafTSQexpo1Jy0MQTBqTAtnzA2bPA58G7geWgGcYRxV0gZXmY5fd/Ukz+zLwfWATGAHPuPuv922E2XXgNvCv/T57DLmfMvsF7fXt/e7eSi6EI5KHAdshmaVRqjxMtSzsq+iPCjO74u6Ptt2OSVNqv6DsvrVJyeNaat+mvV9RSjAIgqBwQtEHQRAUzjQp+gttN+CQKLVfUHbf2qTkcS21b1Pdr6nx0QdBEASHwzRZ9EEQBMEh0LqiN7NzZva6mb1pZk+13Z6DYmZ/N7NXzewPZnaluXafmV0yszea52NRD8/MnjWzZTN7Lbt2x77YmB82v+OfzOyj7bX8+FKSPIQsTI8stKrozewE8CPgMeBh4Gtm9nCbbZoQn2myFSrc6ingJXc/C7zUvD8OXOSdmRr36stjjA8EnWV8lP/HR9TGYihUHkIWpkAW2rboPw686e5vufsG8DPgfMttOgzOA881r58DvtRiW+4ad38ZWN11ea++nAd+4mMuA3Nm9p6jaWkx/D/IQ8hCC7St6N8L/CN7/8/m2nHGgRfN7JUmURXAA+7+dvP6GvBAO02bCHv1pcTf8qgpbQxDFsa0/jtOTSnBgvikuy+a2Sngkpn9Nf+ju3spSatK6ktwKIQsTAltW/SLwPuy96eba8cWd19snpeBXzBeji9p6dY8L7fXwgOzV1+K+y1boKgxDFlItP47tq3ofwecNbMHzawDfBV4oeU23TNm1jOzSq+BzwGvMe7T483HHgd+1U4LJ8JefXkB+HoTcfAJ4Ga2rA3ujmLkIWRhymRBudvbegCfZ1xncwF4uu32HLAvDwF/bB5/Vn+AdzHelX8D+A1wX9ttvcv+PM+4eMYmYz/jt/fqC2CMI0YWgFeBR9tu/3F8lCIPIQvTJQtxMjYIgqBw2nbdBEEQBIdMKPogCILCCUUfBEFQOKHogyAICicUfRAEQeGEog+CICicUPRBEASFE4o+CIKgcP4LDHkSlNW2bAUAAAAASUVORK5CYII=\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 }
 ],
 "source": [
 "r = random.randint(0, len(x)-1)\n",
 "\n",
 "fig = plt.figure()\n",
 "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
 "ax = fig.add_subplot(1, 2, 1)\n",
 "ax.imshow(x[r])\n",
 "ax = fig.add_subplot(1, 2, 2)\n",
 "ax.imshow(np.reshape(y[r]*255, (image_size, image_size)), cmap=\"gray\")"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## Different Blocks"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 9,
 "metadata": {},
 "outputs": [],
 "source": [
 "def bn_act(x, act=True):\n",
 " x = keras.layers.BatchNormalization()(x)\n",
 " if act == True:\n",
 " x = keras.layers.Activation(\"relu\")(x)\n",
 " return x\n",
 "\n",
 "def conv_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
 " conv = bn_act(x)\n",
 " conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)\n",
 " return conv\n",
 "\n",
 "def stem(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
 " conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)\n",
 " conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
 " \n",
 " shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
 " shortcut = bn_act(shortcut, act=False)\n",
 " \n",
 " output = keras.layers.Add()([conv, shortcut])\n",
 " return output\n",
 "\n",
 "def residual_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
 " res = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
 " res = conv_block(res, filters, kernel_size=kernel_size, padding=padding, strides=1)\n",
 " \n",
 " shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
 " shortcut = bn_act(shortcut, act=False)\n",
 " \n",
 " output = keras.layers.Add()([shortcut, res])\n",
 " return output\n",
 "\n",
 "def upsample_concat_block(x, xskip):\n",
 " u = keras.layers.UpSampling2D((2, 2))(x)\n",
 " c = keras.layers.Concatenate()([u, xskip])\n",
 " return c"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## ResUNet"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 10,
 "metadata": {},
 "outputs": [],
 "source": [
 "def ResUNet():\n",
 " f = [16, 32, 64, 128, 256]\n",
 " inputs = keras.layers.Input((image_size, image_size, 3))\n",
 " \n",
 " ## Encoder\n",
 " e0 = inputs\n",
 " e1 = stem(e0, f[0])\n",
 " e2 = residual_block(e1, f[1], strides=2)\n",
 " e3 = residual_block(e2, f[2], strides=2)\n",
 " e4 = residual_block(e3, f[3], strides=2)\n",
 " e5 = residual_block(e4, f[4], strides=2)\n",
 " \n",
 " ## Bridge\n",
 " b0 = conv_block(e5, f[4], strides=1)\n",
 " b1 = conv_block(b0, f[4], strides=1)\n",
 " \n",
 " ## Decoder\n",
 " u1 = upsample_concat_block(b1, e4)\n",
 " d1 = residual_block(u1, f[4])\n",
 " \n",
 " u2 = upsample_concat_block(d1, e3)\n",
 " d2 = residual_block(u2, f[3])\n",
 " \n",
 " u3 = upsample_concat_block(d2, e2)\n",
 " d3 = residual_block(u3, f[2])\n",
 " \n",
 " u4 = upsample_concat_block(d3, e1)\n",
 " d4 = residual_block(u4, f[1])\n",
 " \n",
 " outputs = keras.layers.Conv2D(1, (1, 1), padding=\"same\", activation=\"sigmoid\")(d4)\n",
 " model = keras.models.Model(inputs, outputs)\n",
 " return model"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 11,
 "metadata": {},
 "outputs": [],
 "source": [
 "smooth = 1.\n",
 "\n",
 "def dice_coef(y_true, y_pred):\n",
 " y_true_f = tf.layers.flatten(y_true)\n",
 " y_pred_f = tf.layers.flatten(y_pred)\n",
 " intersection = tf.reduce_sum(y_true_f * y_pred_f)\n",
 " return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n",
 "\n",
 "\n",
 "def dice_coef_loss(y_true, y_pred):\n",
 " return 1.0 - dice_coef(y_true, y_pred)"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 12,
 "metadata": {
 "scrolled": false
 },
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "__________________________________________________________________________________________________\n",
 "Layer (type) Output Shape Param # Connected to \n",
 "==================================================================================================\n",
 "input_1 (InputLayer) (None, 128, 128, 3) 0 \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization (BatchNorma (None, 128, 128, 3) 12 input_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation (Activation) (None, 128, 128, 3) 0 batch_normalization[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_2 (Conv2D) (None, 128, 128, 16) 64 input_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_1 (Conv2D) (None, 128, 128, 16) 448 activation[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_1 (BatchNor (None, 128, 128, 16) 64 conv2d_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add (Add) (None, 128, 128, 16) 0 conv2d_1[0][0] \n",
 " batch_normalization_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_2 (BatchNor (None, 128, 128, 16) 64 add[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_1 (Activation) (None, 128, 128, 16) 0 batch_normalization_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_3 (Conv2D) (None, 64, 64, 32) 4640 activation_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_3 (BatchNor (None, 64, 64, 32) 128 conv2d_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_5 (Conv2D) (None, 64, 64, 32) 544 add[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_2 (Activation) (None, 64, 64, 32) 0 batch_normalization_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_4 (BatchNor (None, 64, 64, 32) 128 conv2d_5[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_4 (Conv2D) (None, 64, 64, 32) 9248 activation_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_1 (Add) (None, 64, 64, 32) 0 batch_normalization_4[0][0] \n",
 " conv2d_4[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_5 (BatchNor (None, 64, 64, 32) 128 add_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_3 (Activation) (None, 64, 64, 32) 0 batch_normalization_5[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_6 (Conv2D) (None, 32, 32, 64) 18496 activation_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_6 (BatchNor (None, 32, 32, 64) 256 conv2d_6[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_8 (Conv2D) (None, 32, 32, 64) 2112 add_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_4 (Activation) (None, 32, 32, 64) 0 batch_normalization_6[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_7 (BatchNor (None, 32, 32, 64) 256 conv2d_8[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_7 (Conv2D) (None, 32, 32, 64) 36928 activation_4[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_2 (Add) (None, 32, 32, 64) 0 batch_normalization_7[0][0] \n",
 " conv2d_7[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_8 (BatchNor (None, 32, 32, 64) 256 add_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_5 (Activation) (None, 32, 32, 64) 0 batch_normalization_8[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_9 (Conv2D) (None, 16, 16, 128) 73856 activation_5[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_9 (BatchNor (None, 16, 16, 128) 512 conv2d_9[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_11 (Conv2D) (None, 16, 16, 128) 8320 add_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_6 (Activation) (None, 16, 16, 128) 0 batch_normalization_9[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_10 (BatchNo (None, 16, 16, 128) 512 conv2d_11[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_10 (Conv2D) (None, 16, 16, 128) 147584 activation_6[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_3 (Add) (None, 16, 16, 128) 0 batch_normalization_10[0][0] \n",
 " conv2d_10[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_11 (BatchNo (None, 16, 16, 128) 512 add_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_7 (Activation) (None, 16, 16, 128) 0 batch_normalization_11[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_12 (Conv2D) (None, 8, 8, 256) 295168 activation_7[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_12 (BatchNo (None, 8, 8, 256) 1024 conv2d_12[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_14 (Conv2D) (None, 8, 8, 256) 33024 add_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_8 (Activation) (None, 8, 8, 256) 0 batch_normalization_12[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_13 (BatchNo (None, 8, 8, 256) 1024 conv2d_14[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_13 (Conv2D) (None, 8, 8, 256) 590080 activation_8[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_4 (Add) (None, 8, 8, 256) 0 batch_normalization_13[0][0] \n",
 " conv2d_13[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_14 (BatchNo (None, 8, 8, 256) 1024 add_4[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_9 (Activation) (None, 8, 8, 256) 0 batch_normalization_14[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_15 (Conv2D) (None, 8, 8, 256) 590080 activation_9[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_15 (BatchNo (None, 8, 8, 256) 1024 conv2d_15[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_10 (Activation) (None, 8, 8, 256) 0 batch_normalization_15[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_16 (Conv2D) (None, 8, 8, 256) 590080 activation_10[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "up_sampling2d (UpSampling2D) (None, 16, 16, 256) 0 conv2d_16[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "concatenate (Concatenate) (None, 16, 16, 384) 0 up_sampling2d[0][0] \n",
 " add_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_16 (BatchNo (None, 16, 16, 384) 1536 concatenate[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_11 (Activation) (None, 16, 16, 384) 0 batch_normalization_16[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_17 (Conv2D) (None, 16, 16, 256) 884992 activation_11[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_17 (BatchNo (None, 16, 16, 256) 1024 conv2d_17[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_19 (Conv2D) (None, 16, 16, 256) 98560 concatenate[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_12 (Activation) (None, 16, 16, 256) 0 batch_normalization_17[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_18 (BatchNo (None, 16, 16, 256) 1024 conv2d_19[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_18 (Conv2D) (None, 16, 16, 256) 590080 activation_12[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_5 (Add) (None, 16, 16, 256) 0 batch_normalization_18[0][0] \n",
 " conv2d_18[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "up_sampling2d_1 (UpSampling2D) (None, 32, 32, 256) 0 add_5[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "concatenate_1 (Concatenate) (None, 32, 32, 320) 0 up_sampling2d_1[0][0] \n",
 " add_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_19 (BatchNo (None, 32, 32, 320) 1280 concatenate_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_13 (Activation) (None, 32, 32, 320) 0 batch_normalization_19[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_20 (Conv2D) (None, 32, 32, 128) 368768 activation_13[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_20 (BatchNo (None, 32, 32, 128) 512 conv2d_20[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_22 (Conv2D) (None, 32, 32, 128) 41088 concatenate_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_14 (Activation) (None, 32, 32, 128) 0 batch_normalization_20[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_21 (BatchNo (None, 32, 32, 128) 512 conv2d_22[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_21 (Conv2D) (None, 32, 32, 128) 147584 activation_14[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_6 (Add) (None, 32, 32, 128) 0 batch_normalization_21[0][0] \n",
 " conv2d_21[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "up_sampling2d_2 (UpSampling2D) (None, 64, 64, 128) 0 add_6[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "concatenate_2 (Concatenate) (None, 64, 64, 160) 0 up_sampling2d_2[0][0] \n",
 " add_1[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_22 (BatchNo (None, 64, 64, 160) 640 concatenate_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_15 (Activation) (None, 64, 64, 160) 0 batch_normalization_22[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_23 (Conv2D) (None, 64, 64, 64) 92224 activation_15[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_23 (BatchNo (None, 64, 64, 64) 256 conv2d_23[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_25 (Conv2D) (None, 64, 64, 64) 10304 concatenate_2[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_16 (Activation) (None, 64, 64, 64) 0 batch_normalization_23[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_24 (BatchNo (None, 64, 64, 64) 256 conv2d_25[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_24 (Conv2D) (None, 64, 64, 64) 36928 activation_16[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_7 (Add) (None, 64, 64, 64) 0 batch_normalization_24[0][0] \n",
 " conv2d_24[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "up_sampling2d_3 (UpSampling2D) (None, 128, 128, 64) 0 add_7[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "concatenate_3 (Concatenate) (None, 128, 128, 80) 0 up_sampling2d_3[0][0] \n",
 " add[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_25 (BatchNo (None, 128, 128, 80) 320 concatenate_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_17 (Activation) (None, 128, 128, 80) 0 batch_normalization_25[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_26 (Conv2D) (None, 128, 128, 32) 23072 activation_17[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_26 (BatchNo (None, 128, 128, 32) 128 conv2d_26[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_28 (Conv2D) (None, 128, 128, 32) 2592 concatenate_3[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "activation_18 (Activation) (None, 128, 128, 32) 0 batch_normalization_26[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "batch_normalization_27 (BatchNo (None, 128, 128, 32) 128 conv2d_28[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_27 (Conv2D) (None, 128, 128, 32) 9248 activation_18[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "add_8 (Add) (None, 128, 128, 32) 0 batch_normalization_27[0][0] \n",
 " conv2d_27[0][0] \n",
 "__________________________________________________________________________________________________\n",
 "conv2d_29 (Conv2D) (None, 128, 128, 1) 33 add_8[0][0] \n",
 "==================================================================================================\n",
 "Total params: 4,720,685\n",
 "Trainable params: 4,713,415\n",
 "Non-trainable params: 7,270\n",
 "__________________________________________________________________________________________________\n"
 ]
 }
 ],
 "source": [
 "model = ResUNet()\n",
 "adam = keras.optimizers.Adam()\n",
 "model.compile(optimizer=adam, loss=dice_coef_loss, metrics=[dice_coef])\n",
 "model.summary()"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "## Training"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 13,
 "metadata": {},
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "Epoch 1/10\n",
 "237/237 [==============================] - 40s 168ms/step - loss: 0.4408 - dice_coef: 0.5592 - val_loss: 0.5942 - val_dice_coef: 0.4058\n",
 "Epoch 2/10\n",
 "237/237 [==============================] - 22s 93ms/step - loss: 0.3808 - dice_coef: 0.6192 - val_loss: 0.5182 - val_dice_coef: 0.4818\n",
 "Epoch 3/10\n",
 "237/237 [==============================] - 22s 94ms/step - loss: 0.3945 - dice_coef: 0.6055 - val_loss: 0.4681 - val_dice_coef: 0.5319\n",
 "Epoch 4/10\n",
 "237/237 [==============================] - 23s 96ms/step - loss: 0.3370 - dice_coef: 0.6630 - val_loss: 0.3781 - val_dice_coef: 0.6219\n",
 "Epoch 5/10\n",
 "237/237 [==============================] - 23s 96ms/step - loss: 0.3830 - dice_coef: 0.6170 - val_loss: 0.4132 - val_dice_coef: 0.5868\n",
 "Epoch 6/10\n",
 "237/237 [==============================] - 23s 98ms/step - loss: 0.3179 - dice_coef: 0.6821 - val_loss: 0.2835 - val_dice_coef: 0.7165\n",
 "Epoch 7/10\n",
 "237/237 [==============================] - 23s 97ms/step - loss: 0.3140 - dice_coef: 0.6860 - val_loss: 0.2471 - val_dice_coef: 0.7529\n",
 "Epoch 8/10\n",
 "237/237 [==============================] - 23s 98ms/step - loss: 0.3042 - dice_coef: 0.6958 - val_loss: 0.5953 - val_dice_coef: 0.4047\n",
 "Epoch 9/10\n",
 "237/237 [==============================] - 24s 100ms/step - loss: 0.2813 - dice_coef: 0.7187 - val_loss: 0.3272 - val_dice_coef: 0.6728\n",
 "Epoch 10/10\n",
 "237/237 [==============================] - 24s 100ms/step - loss: 0.2656 - dice_coef: 0.7344 - val_loss: 0.2607 - val_dice_coef: 0.7393\n"
 ]
 },
 {
 "data": {
 "text/plain": [
 "<tensorflow.python.keras.callbacks.History at 0x7f2a8422a828>"
 ]
 },
 "execution_count": 13,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "train_gen = DataGen(train_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
 "valid_gen = DataGen(valid_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
 "\n",
 "train_steps = len(train_ids)//batch_size\n",
 "valid_steps = len(valid_ids)//batch_size\n",
 "\n",
 "epochs = 10\n",
 "\n",
 "model.fit_generator(train_gen, validation_data=valid_gen, steps_per_epoch=train_steps, validation_steps=valid_steps, \n",
 " epochs=epochs)"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 14,
 "metadata": {},
 "outputs": [],
 "source": [
 "model.save_weights(\"ResUNet.h5\")"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 15,
 "metadata": {
 "scrolled": false
 },
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\n",
 " Ground Truth Predicted Value\n"
 ]
 },
 {
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/home/nikhil/.local/lib/python3.6/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
 " max_open_warning, RuntimeWarning)\n"
 ]
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
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 },
 "output_type": "display_data"
 },
 {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
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 },
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
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 },
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
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 },
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
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kwrW96UaS1DCDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwv0/JR43GdEbSVoAAAAASUVORK5CYII=\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
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 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
 "output_type": "display_data"
 },
 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
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 },
 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
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 },
 "output_type": "display_data"
 },
 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "data": {
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
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 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
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 },
 "output_type": "display_data"
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 {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
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 "output_type": "display_data"
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 {
 "data": {
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\n",
 "text/plain": [
 "<Figure size 432x288 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 }
 ],
 "source": [
 "print(\"\\n Ground Truth Predicted Value\")\n",
 "\n",
 "for i in range(1, 5, 1):\n",
 " ## Dataset for prediction\n",
 " x, y = valid_gen.__getitem__(i)\n",
 " result = model.predict(x)\n",
 " result = result > 0.4\n",
 " \n",
 " for i in range(len(result)):\n",
 " fig = plt.figure()\n",
 " fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
 "\n",
 " ax = fig.add_subplot(1, 2, 1)\n",
 " ax.imshow(np.reshape(y[i]*255, (image_size, image_size)), cmap=\"gray\")\n",
 "\n",
 " ax = fig.add_subplot(1, 2, 2)\n",
 " ax.imshow(np.reshape(result[i]*255, (image_size, image_size)), cmap=\"gray\")"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": null,
 "metadata": {},
 "outputs": [],
 "source": []
 },
 {
 "cell_type": "code",
 "execution_count": null,
 "metadata": {},
 "outputs": [],
 "source": []
 }
 ],
 "metadata": {
 "kernelspec": {
 "display_name": "Python 3",
 "language": "python",
 "name": "python3"
 },
 "language_info": {
 "codemirror_mode": {
 "name": "ipython",
 "version": 3
 },
 "file_extension": ".py",
 "mimetype": "text/x-python",
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.6.7"
 }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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提交于 2019-02-20 18:07 . Update Deep Residual UNet.ipynb

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